Fuzzy logic and histogram of normal orientation-based 3D keypoint detection for point clouds

Iqbal, Muhammad; Bobkov, Dmytro; Steinbach, Eckehard

doi:10.1016/j.patrec.2020.05.010

Cited by 3 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fast point feature histogram (FPFH) [31] is an improvement of a point feature histogram (PFH) [32]. The idea of it is to form specific parameters to describe the changes in the object surface by parameterizing the interaction between the normal features of a point in the point cloud and the normal vector of its neighboring points.…”

Section: Establish Point Cloud Feature Descriptormentioning

confidence: 99%

“…Compared with the global fe descriptor, the local feature descriptor has a higher fault tolerance and is less subje point cloud rotation and density reduction. A fast point feature histogram (FPFH) [31] is an improvement of a point featur togram (PFH) [32]. The idea of it is to form specific parameters to describe the chang the object surface by parameterizing the interaction between the normal features of a in the point cloud and the normal vector of its neighboring points.…”

Section: Establish Point Cloud Feature Descriptormentioning

confidence: 99%

See 1 more Smart Citation

3D Pose Recognition of Small Special-Shaped Sheet Metal with Multi-Objective Overlapping

et al. 2023

View full text Add to dashboard Cite

This paper addresses the challenging task of determining the position and posture of small-scale thin metal parts with multi-objective overlapping. To tackle this problem, we propose a method that utilizes instance segmentation and a three-dimensional (3D) point cloud for recognizing the posture of thin special-shaped metal parts. We investigate the process of obtaining a single target point cloud by aligning the target mask with the depth map. Additionally, we explore a pose estimation method that involves registering the target point cloud with the model point cloud, designing a registration algorithm that combines the sample consensus initial alignment algorithm (SAC-IA) for coarse registration and the iterative closest point (ICP) algorithm for fine registration. The experimental results demonstrate the effectiveness of our approach. The average accuracy of the instance segmentation models, utilizing ResNet50 + FPN and ResNet101 + FPN as backbone networks, exceeds 97%. The time consumption of the ResNet50 + FPN model is reduced by 50%. Furthermore, the registration algorithm, which combines the SAC-IA and ICP, achieves a lower average consumption time while satisfying the requirements for the manufacturing of new energy batteries.

show abstract

Section: Establish Point Cloud Feature Descriptormentioning

confidence: 99%

Section: Establish Point Cloud Feature Descriptormentioning

confidence: 99%

3D Pose Recognition of Small Special-Shaped Sheet Metal with Multi-Objective Overlapping

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In the realm of keypoint detection based on normal vectors, Prakhya et al [17] presented a keypoint detection algorithm based on surface transformation and eigenvalue change indices, which combines the advantages of ISS and KPQ to improve repeatability. Also in the realm of keypoint detection based on normal vectors, Muhammad et al [10] introduced the fuzzy logic and normal orientation (Fuzzy-HoNo) algorithm, which enhances keypoint detection through soft decision boundaries and adaptive parameters. While it improves keypoint detection, this algorithm does entail higher computational complexity.…”

Section: Related Workmentioning

confidence: 99%

“…However, each of these methods has its limitations in practical applications. For instance, LSP detects keypoints uniformly but with relatively low repeatability [10], and KPQ tends to perform poorly in scenarios involving partial occlusion [6]. On the other hand, adaptive-size detection algorithms generate multiple scales for the input point cloud and detect keypoints at different scales, as observed in MeshDog and KPQ-AS [9].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, adaptive-size detection algorithms generate multiple scales for the input point cloud and detect keypoints at different scales, as observed in MeshDog and KPQ-AS [9]. Adaptive-size detection algorithms generally outperform fixed-size approaches [10], but they come with added complexity in scale computation, resulting in increased computational overhead. In contrast, fixed-size detection algorithms are computationally more efficient due to their simplicity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Point Cloud Registration Based on Local Variation of Surface Keypoints

Zhu,

Huang,

Yue

et al. 2023

Electronics

View full text Add to dashboard Cite

Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency.

show abstract